{
 "cells": [
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   "cell_type": "code",
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     "text": "L2 norm of w: 12.795610427856445\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn \n",
    "import numpy as np \n",
    "import d2lzh as d2l\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n_train,n_test,num_inputs = 20,100,200\n",
    "true_w,true_b = torch.ones(num_inputs,1) * 0.01,0.05\n",
    "\n",
    "features = torch.randn((n_train + n_test,num_inputs))\n",
    "labels = torch.matmul(features,true_w) + true_b \n",
    "labels += torch.tensor(np.random.normal(0,0.01,size=labels.size()),dtype=torch.float32)\n",
    "\n",
    "train_features,test_features = features[:n_train,:],features[n_train:,:]\n",
    "train_labels,test_labels = labels[:n_train],labels[n_train:]\n",
    "\n",
    "def init_params():\n",
    "    w = torch.randn((num_inputs,1),requires_grad=True)\n",
    "    b = torch.zeros(1,requires_grad=True)\n",
    "\n",
    "    return [w,b]\n",
    "\n",
    "def l2_penalty(w):\n",
    "    return (w**2).sum() / 2\n",
    "\n",
    "def linreg(X,w,b):\n",
    "    return torch.mm(X,w) + b \n",
    "\n",
    "def squared_loss(y_hat,y):\n",
    "    return (y_hat - y.view(y_hat.size()))**2 / 2\n",
    "\n",
    "batch_size = 1\n",
    "num_epochs = 100\n",
    "lr = 0.003\n",
    "\n",
    "net = linreg\n",
    "loss = squared_loss\n",
    "\n",
    "dataset = torch.utils.data.TensorDataset(train_features,train_labels)\n",
    "train_iter = torch.utils.data.DataLoader(dataset,batch_size,shuffle=True)\n",
    "\n",
    "def fit_and_plot(lam):\n",
    "    w,b = init_params()\n",
    "    train_ls,test_ls = [],[]\n",
    "\n",
    "    for _ in range(num_epochs):\n",
    "        for X,y in train_iter:\n",
    "\n",
    "            l = loss(net(X,w,b),y) + lam * l2_penalty(w) # l2　惩罚项\n",
    "            l = l.sum()\n",
    "\n",
    "            if w.grad is not None:\n",
    "                w.grad.data.zero_()\n",
    "                b.grad.data.zero_()\n",
    "\n",
    "            l.backward()\n",
    "            d2l.sgd([w,b],lr,batch_size)\n",
    "        \n",
    "        train_ls.append(loss(net(train_features ,w,b),train_labels).mean().item())\n",
    "        test_ls.append(loss(net(test_features,w,b),test_labels).mean().item())\n",
    "\n",
    "    d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','loss',range(1,num_epochs+1),test_ls,['train','test'])\n",
    "\n",
    "    print('L2 norm of w:',w.norm().item())\n",
    "\n",
    "fit_and_plot(lam=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "L2 norm of w: 0.02826516330242157\n"
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fit_and_plot(lam=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "L2 norm of w: 13.88662338256836\n"
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def fit_and_plot_pytorch(wd):\n",
    "\n",
    "    net = nn.Linear(num_inputs,1)\n",
    "    nn.init.normal_(net.weight,mean=0,std=1)\n",
    "    nn.init.constant_(net.bias,val=0)\n",
    "\n",
    "    optimizer_w = torch.optim.SGD(params=[net.weight],lr=lr,weight_decay=wd)\n",
    "    optimizer_b = torch.optim.SGD(params=[net.bias],lr=lr) # 不使用衰减\n",
    "\n",
    "    train_ls,test_ls = [],[]\n",
    "\n",
    "    for _ in range(num_epochs):\n",
    "        for X,y in train_iter:\n",
    "            l = loss(net(X),y).mean()\n",
    "            optimizer_b.zero_grad()\n",
    "            optimizer_w.zero_grad()\n",
    "\n",
    "            l.backward()\n",
    "\n",
    "            optimizer_w.step()\n",
    "            optimizer_b.step()\n",
    "        train_ls.append(loss(net(train_features),train_labels).mean().item())\n",
    "        test_ls.append(loss(net(test_features),test_labels).mean().item())\n",
    "\n",
    "    d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','loss',range(1,num_epochs+1),test_ls,['train','test'])\n",
    "\n",
    "    print('L2 norm of w:',net.weight.data.norm().item())\n",
    "\n",
    "fit_and_plot_pytorch(0)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "L2 norm of w: 0.035129718482494354\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fit_and_plot_pytorch(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "- 正则化通过对模型损失函数添加惩罚项使学出的模型参数值较小，市应对过拟合的常用手段　\n",
    "- 权重衰减等价于$L_2$范数，通常会使得学到的权重参数的值接近于０．　\n",
    "- 可以定义多个优化器实现对不同模型参数使用不同的迭代方法。"
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37764bitpytorchnotebookconda6e7a8693df0d4d92aca09d521275d23a",
   "display_name": "Python 3.7.7 64-bit ('pytorch_notebook': conda)"
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 },
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 "nbformat_minor": 2
}